We consider the following task proposal methods: Frequency (FREQ, c1): using the frequency-based word sampling in SMLMT; Inter-Cluster (X-C, c2): using the word-clustering approach explained in sec REF  but sampling all labels of task from different clusters; Intra-Cluster (I-C, c3&4): using the word-clustering approach explained in sec REF  which samples all labels of task from the same cluster; Sentence Cluster (S-C, c5): this is the sentence clustering approach to task proposal presented in sec REF .
For evaluation, we consider 4-way tasks sampled from the above methods and evaluate average accuracy over 5000 tasks.
We consider a BERT model (r1) which is not trained on the SMLMT distribution but is trained on the related masked language modeling (MLM) task. To enable evaluation of this model, we use it as a prototypical network model [1]}.
We also consider meta-trained models trained on the SMLMT distribution with uniform sampling [2]} (r2), frequency-based sampling (r3), and intra-cluster sampling (r4).
Note that all models are trained on Wikipedia corpus.
<TABLE>